Day-24 Computer Vision Learning Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization Follow me for similar post : 🇮🇳 Ashish Patel 𝗜𝗻𝘁𝗲𝗿𝗲𝘀𝘁𝗶𝗻𝗴 𝗙𝗮𝗰𝘁𝘀 : 🔸 Grad-CAM : Weakly Supervised Object Localization Technique. 🔸 This is a paper in ICCV 2017 with More than 3647 Citation 🔸 Grad-CAM is applicable to a wide variety of CNN model-families. 🔸 It creates a class-discriminative visualization. 🔸 It also lend insights into failure modes of the model. ------------------------------------------------------------------- 𝗔𝗺𝗮𝘇𝗶𝗻𝗴 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 : https://lnkd.in/ensn6q2 Lua Programming: https://bit.ly/2MgJygO keras : https://bit.ly/2KIPHlr Tensorflow : https://bit.ly/3iEQL6r Pytorch : https://bit.ly/365HyPk, https://bit.ly/3sLcQES ------------------------------------------------------------------- 𝗜𝗠𝗣𝗢𝗥𝗧𝗔𝗡𝗖𝗘 🔸 Grad-CAM uses the gradient information flowing into the last convolutional layer of the CNN to understand the importance of each neuron for a decision of interest. 🔸 The Result is heatmap image is generate the same size as the convolution feature map generate. More in comments #innovation #artificialintelligence #computervision
Hello, do you know how this heat maps are produced? Is it just the visualization of an activation map on a certain layer? Any hint on this is highly welcome - this could help debugging a model.
Awesome computer vision series going on! Keep it up!
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🔸 Guided Grad-CAM (in Result Analysis) is produced by fusing Guided Backpropagation (Backprop) and Grad-CAM visualizations via pointwise multiplication (LcGrad-CAM is first up-sampled to the input image resolution using bi-linear interpolation). 🔸 Four techniques: Deconvolution, Guided Backpropagation, and Grad-CAM versions of each these methods (Deconvolution Grad-CAM and Guided Grad-CAM). 🔸 It is used for Image classification, Captioning, Comparing Densemap etc.
Hello, i really read this but i only get like 20% of it. Can i ask some basic questions please? I need to implement this to debug a model using opencv dnn module. Some very basic concepts - to check my understanding: *Convolution* - The process of applying a filter on the data *Feature Map* - This is the same as an activation map. The result of applying a (learned?!) filter on the output of the previous layer (first layer is input layer - which is the raw image data). A layer can have multiple filters - this will result in multiple feature maps. Implementation Idea: Forward to the last conv layer of the model and grab all feature maps. Resize them to input image dimensions and produce a heatmap based on pixel intensity. Am i thinking to simple? This is not the opencv or a discussion forum but i hope i can at least verify if i am on the right track. Thank you very much.